5aad1eb846
Bundles all v2 components into one cohesive trainer field that
PerceptionTrainer wires through in V10. Owns:
(C) horizon_tokens + Q for the horizon-token attention pool
+ per-batch grad scratches + 2 AdamWs
(E) inverted attention pool + saved attn + d_scores scratch
(D) MoE gate weights + experts (W, b) + per-batch sparse-by-expert
grad scratches + 3 AdamWs + top_e / gate_probs / aux_loss
(A) log_sigma_h per-horizon Kendall scalar + AdamW (0.25× LR)
(B) AnchorController (Wiener-α host-side) + anchor_l2 kernel +
init snapshots of horizon_tokens, Q, experts_w, experts_b
for the L2 anchor
Init scale: 1/√HIDDEN_DIM Xavier for horizon_tokens/Q/experts_W;
zeros for experts_b and log_sigma_h. Anchor controller bootstrap
uses ‖horizon_tokens_init‖₂ + ‖Q_init‖₂ + their total numel to
derive the signal-driven floor (no tuned constants).
zero_grads uses memset_zeros only — capture-safe.
Compile-clean. V10 wires this state into perception.rs::step_batched
forward / backward / AdamW.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.